DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber Sensing.

Optical fiber sensors are extensively employed for their unique merits, such as small size, being lightweight, and having strong robustness to electronic interference. The above-mentioned sensors apply to more applications, especially the detection and monitoring of vital signs in medical or clinical. However, it is inconvenient for daily long-term human vital sign monitoring with conventional monitoring methods under the uncomfortable feelings generated since the skin and devices come into direct contact. This study introduces a non-invasive surveillance system that employs an optical fiber sensor and advanced deep-learning methodologies for precise vital sign readings. This system integrates a monitor based on the MZI (Mach-Zehnder interferometer) with LSTM networks, surpassing conventional approaches and providing potential uses in medical diagnostics. This could be potentially utilized in non-invasive health surveillance, evaluation, and intelligent health care.

Ballistocardial Signal-Based Personal Identification Using Deep Learning for the Non-Invasive and Non-Restrictive Monitoring of Vital Signs.

Owing to accelerated societal aging, the prevalence of elderly individuals experiencing solitary or sudden death at home has increased. Therefore, herein, we aimed to develop a monitoring system that utilizes piezoelectric sensors for the non-invasive and non-restrictive monitoring of vital signs, including the heart rate and respiration, to detect changes in the health status of several elderly individuals. A ballistocardiogram with a piezoelectric sensor was tested using seven individuals. The frequency spectra of the biosignals acquired from the piezoelectric sensors exhibited multiple peaks corresponding to the harmonics originating from the heartbeat. We aimed for individual identification based on the shapes of these peaks as the recognition criteria. The results of individual identification using deep learning techniques revealed good identification proficiency. Altogether, the monitoring system integrated with piezoelectric sensors showed good potential as a personal identification system for identifying individuals with abnormal biological signals.

A transformer-based diffusion probabilistic model for heart rate and blood pressure forecasting in Intensive Care Unit.

BACKGROUND AND OBJECTIVE: Vital sign monitoring in the Intensive Care Unit (ICU) is crucial for enabling prompt interventions for patients. This underscores the need for an accurate predictive system. Therefore, this study proposes a novel deep learning approach for forecasting Heart Rate (HR), Systolic Blood Pressure (SBP), and Diastolic Blood Pressure (DBP) in the ICU. METHODS: We extracted 24,886 ICU stays from the MIMIC-III database which contains data from over 46 thousand patients, to train and test the model. The model proposed in this study, Transformer-based Diffusion Probabilistic Model for Sparse Time Series Forecasting (TDSTF), merges Transformer and diffusion models to forecast vital signs. The TDSTF model showed state-of-the-art performance in predicting vital signs in the ICU, outperforming other models' ability to predict distributions of vital signs and being more computationally efficient. The code is available at https://github.com/PingChang818/TDSTF. RESULTS: The results of the study showed that TDSTF achieved a Standardized Average Continuous Ranked Probability Score (SACRPS) of 0.4438 and a Mean Squared Error (MSE) of 0.4168, an improvement of 18.9% and 34.3% over the best baseline model, respectively. The inference speed of TDSTF is more than 17 times faster than the best baseline model. CONCLUSION: TDSTF is an effective and efficient solution for forecasting vital signs in the ICU, and it shows a significant improvement compared to other models in the field.

Vital Sign Monitoring for Cancer Patients Based on Dual-Path Sensor and Divided-Frequency-CNN Model.

Monitoring vital signs is a key part of standard medical care for cancer patients. However, the traditional methods have instability especially when big fluctuations of signals happen, while the deep-learning-based methods lack pertinence to the sensors. A dual-path micro-bend optical fiber sensor and a targeted model based on the Divided-Frequency-CNN (DFC) are developed in this paper to measure the heart rate (HR) and respiratory rate (RR). For each path, features of frequency division based on the mechanism of signal periodicity cooperate with the operation of stable phase extraction to reduce the interference of body movements for monitoring. Then, the DFC model is designed to learn the inner information from the features robustly. Lastly, a weighted strategy is used to estimate the HR and RR via dual paths to increase the anti-interference for errors from one source. The experiments were carried out on the actual clinical data of cancer patients by a hospital. The results show that the proposed method has good performance in error (3.51 (4.51 %) and 2.53 (3.28 %) beats per minute (bpm) for cancer patients with pain and without pain respectively), relevance, and consistency with the values from hospital equipment. Besides, the proposed method significantly improved the ability in the report time interval (30 to 9 min), and mean / confidential interval (3.60/[-22.61,29.81] to -0.64 / [-9.21,7.92] for patients with pain and 1.87 / [-5.49,9.23] to -0.16 / [-6.21,5.89] for patients without pain) compared with our previous work.

Contactless face video based vital signs detection framework for continuous health monitoring using feature optimization and hybrid neural network.

Continuous monitoring of vital signs such as respiration and heart rate is essential to detect and predict conditions that may affect the patient's well-being. To detect these vital signs most medical systems use contact sensors. They are not feasible for long term monitoring and are not repeatable. Vital signs using facial video-noncontact monitoring are becoming increasingly important. Researchers in the last few years although considerable progress has been made, challenging datasets absence timing of assessment process and the technology still has some limitations such as time consuming nature and lack of computer portability. To solve those problems, we propose a contactless video based vital signs detection framework for continuous health monitoring using feature optimization and hybrid neural network. In the proposed technique, modified war strategy optimization algorithm is proposed to segment the face portion from the input video frames. Then, we utilize the known data acquisition models to extract vital signs from the segmented face portions are heart rate, blood pressure, respiratory rate and oxygen saturation. An improved neural network structure (Lifting Net) is further used to achieve the adaptive extraction of deep hidden features for specific signs, for realizing the high precision of human health monitoring. The Hughes effect or dimensionality issue affects detection accuracy in sign classification when there are fewer training instances relative to the number of spectral features. The problem can be overcome through feature optimization here Northern goshawk optimization algorithm is used to select optimal best features which reduces the data dimensionality issue. Furthermore, hybrid deep ensemble reinforcement learning classifier is proposed for the human vital sign detection and classification which ensures the early detection of patient abnormality. Finally, we validate our framework using benchmark video datasets such as TokyoTechrPPG, PURE and COHFACE. To proves the effectiveness of proposed technique using simulation results and comparative analysis.

Impact of wearable wireless continuous vital sign monitoring in abdominal surgical patients: before-after study.

BACKGROUND: Technological advances have enabled continuous monitoring of vital signs (CMVS) by wearable, wireless devices on general hospital wards to facilitate early detection of clinical deterioration, which could potentially improve clinical outcomes. However, evidence on the impact of these CMVS systems on patient outcomes is limited. This research aimed to explore the effect of CMVS on the clinical outcomes in major abdominal surgery patients in a general surgery ward. METHODS: A single-centre before-after study was conducted from October 2019 to June 2022. Patients in the intervention group received CMVS in addition to conventional intermittent vital sign monitoring (standard care for control group). With CMVS, heart rate and respiratory rate were measured every 5 min by a patch sensor. Proactive vital signs trends assessments and, when necessary, subsequent nursing activities were performed every nursing shift. The primary outcome of interest was the length of hospital stay (LOS); also, 12 patient-related outcomes were analysed. In the CMVS group, follow-up nursing activities of deviating vital signs trends were described and patient acceptability was measured. Post-hoc subgroup analysis was performed for colorectal and hepatopancreatobiliary surgery. RESULTS: A total of 908 patients were included (colorectal: n = 650; hepatopancreatobiliary: n = 257). Overall, median LOS was lower in the CMVS group (5.0 versus 5.5 days; P = 0.012), respectively. Post-hoc subgroup analysis showed this reduction in LOS was mostly observed in the colorectal group and not in the hepatopancreatobiliary group. Apart from a decrease in nurse-to-house-officer calls (from 15.3% to 7.7%; P = 0.007), all secondary clinical outcomes were similar in CMVS and control groups. However, a non-significant trend towards less-severe complications and reduced ICU LOS was observed in the CMVS group. In CMVS patients, 109 additional nursing activities were performed and 83% of patients indicated CMVS was acceptable. CONCLUSION: CMVS was associated with a significant reduction in LOS, while other clinical outcomes were unchanged. CMVS triggered additional nursing activities such as extra patient assessments and therapeutic interventions.

Early Abnormal Vital Signs Predict Poor Outcomes in Normotensive Patients Following Penetrating Trauma.

INTRODUCTION: Because trauma patients in class II shock (blood loss of 15%-30% of total blood volume) arrive normotensive, this makes the identification of shock and subsequent prognostication of outcomes challenging. Our aim was to identify early predictive factors associated with worse outcomes in normotensive patients following penetrating trauma. We hypothesize that abnormalities in initial vital signs portend worse outcomes in normotensive patients following penetrating trauma. METHODS: A retrospective review was performed from 2006 to 2021 using our trauma database and included trauma patients presenting with penetrating trauma with initial normotensive blood pressures (systolic blood pressure ≥90 mmHg). We compared those with a narrow pulse pressure (NPP ≤25% of systolic blood pressure), tachycardia (heart rate ≥100 beats per minute), and elevated shock index (SI ≥ 0.8) to those without. Outcomes included mortality, intensive care unit admission, and ventilator use. Chi-squared, Mann-Whitney tests, and regression analyses were performed as appropriate. RESULTS: We identified 7618 patients with penetrating injuries and normotension on initial trauma bay assessment. On univariate analysis, NPP, tachycardia, and elevated SI were associated with increases in mortality compared to those without. On multivariable logistic regression, only NPP and tachycardia were independently associated with mortality. Tachycardia and an elevated SI were both independently associated with intensive care unit admission. Only an elevated SI had an independent association with ventilator requirements, while an NPP and tachycardia did not. CONCLUSIONS: Immediate trauma bay NPP and tachycardia are independently associated with mortality and adverse outcomes and may provide an opportunity for improved prognostication in normotensive patients following penetrating trauma.

Establishing outcome-driven vital signs ranges for children in the prehospital setting.

BACKGROUND: Vital signs are frequently used in pediatric prehospital assessments and guide protocol utilization. Common pediatric vital sign classification criteria identify >80% of children in the prehospital setting as having abnormal vital signs, though few receive lifesaving interventions (LSIs). We sought to identify data-driven thresholds for abnormal vital signs by evaluating their association with prehospital LSIs. METHODS: We evaluated prehospital care records for children (<18 years) transported to the hospital during 2022 from a large, national repository of emergency medical services (EMS) patient encounters. Predictors of interest were heart rate (HR), respiratory rate (RR), systolic blood pressure (SBP), and pulse oximetry. HR, RR, and SBP were converted to Z-scores using age-based distributional models. Our outcome was potential LSIs, defined as performance of selected respiratory procedures, resuscitative interventions, or medication administrations. Using cut point analysis, we identified higher specificity (maximal specificity with a minimum of 25% sensitivity) and higher sensitivity (maximal sensitivity with a minimum of 25% specificity) ranges for each vital sign and evaluated measures of diagnostic accuracy. RESULTS: We included 987,515 children (median age 10 years, IQR 2-15 years). An LSI occurred in 4.3% (2.1% with respiratory procedures, 1.2% with resuscitative interventions, and 2.0% with medication administration). HR, RR, and SBP demonstrated a U-shaped association with LSIs. Specificities ranged from 84.1% to 93.7% for higher specificity criteria, with RR demonstrating the best performance (sensitivity 84.6%, specificity 27.0%). Sensitivities ranged from 62.3% to 84.4% for higher sensitivity criteria. CONCLUSIONS: Cut points for pediatric vital signs were associated with LSIs. Specific age-adjusted ranges can identify children at higher and lower risk for receipt of LSI. These ranges may be combined with other objective measures to improve the assessment of children in the prehospital setting, assist in optimizing protocol utilization, improve transport decision making, and guide destination selection.

Workload associated with manual assessment of vital signs as compared with continuous wireless monitoring.

BACKGROUND: Vital sign monitoring is considered an essential aspect of clinical care in hospitals. In general wards, this relies on intermittent manual assessments performed by clinical staff at intervals of up to 12 h. In recent years, continuous monitoring of vital signs has been introduced to the clinic, with improved patient outcomes being one of several potential benefits. The aim of this study was to determine the workload difference between continuous monitoring and manual monitoring of vital signs as part of the National Early Warning Score (NEWS). METHODS: Three wireless sensors continuously monitored blood pressure, heart rate, respiratory rate, and peripheral oxygen saturation in 20 patients admitted to the general hospital ward. The duration needed for equipment set-up and maintenance for continuous monitoring in a 24-h period was recorded and compared with the time spent on manual assessments and documentation of vital signs performed by clinical staff according to the NEWS. RESULTS: The time used for continuous monitoring was 6.0 (IQR 3.2; 7.2) min per patient per day vs. 14 (9.7; 32) min per patient per day for the NEWS. Median difference in duration for monitoring of vital signs was 9.9 (95% CI 5.6; 21) min per patient per day between NEWS and continuous monitoring (p < .001). Time used for continuous monitoring in isolated patients was 6.6 (4.6; 12) min per patient per day as compared with 22 (9.7; 94) min per patient per day for NEWS. CONCLUSION: The use of continuous monitoring was associated with a significant reduction in workload in terms of time for monitoring as compared with manual assessment of vital signs.

Frequency of and associations with alterations of medical emergency team calling criteria in a teaching hospital emergency department.

BACKGROUND: Medical emergency team (METs), activated by vital sign-based calling criteria respond to deteriorating patients in the hospital setting. Calling criteria may be altered where clinicians feel this is appropriate. Altered calling criteria (ACC) has not previously been evaluated in the emergency department (ED) setting. OBJECTIVES: The objectives of this study were to (i) describe the frequency of ACC in a teaching hospital ED and the number and type of vital signs that were modified and (ii) associations between ACC in the ED and differences in the baseline patient characteristics and adverse outcomes including subsequent MET activations, unplanned intensive care unit (ICU) admissions and death within 72 h of admission. METHODS: Retrospective observational study of patients presenting to an academic, tertiary hospital ED in Melbourne, Australia between January 1st, 2019 and December 31st, 2019. The primary outcome was frequency and nature of ACC in the ED. Secondary outcomes included differences in baseline patient characteristics, frequency of MET activation, unplanned ICU admission, and mortality in the first 72 h of admission between those with and without ACC in the ED. RESULTS: Amongst 14 159 ED admissions, 725 (5.1%) had ACC, most frequently for increased heart or respiratory rate. ACC was associated with older age and increased comorbidity. Such patients had a higher adjusted risk of MET activation (odds ratio [OR]: 3.14, 95% confidence interval [CI]: 2.50-3.91, p = <0.001), unplanned ICU admission (OR: 1.97, 95% CI: 1.17-3.14, p = 0.016), and death (OR: 3.87, 95% CI: 2.08-6.70, p = 0.020) within 72 h. CONCLUSIONS: ACC occurs commonly in the ED, most frequently for elevated heart and respiratory rates and is associated with worse patient outcomes. In some cases, ACC requires consultant involvement, more frequent vital sign monitoring, expeditious inpatient team review, or ICU referral.

Impact of continuous and wireless monitoring of vital signs on clinical outcomes: a propensity-matched observational study of surgical ward patients.

BACKGROUND: Continuous and wireless vital sign monitoring is superior to intermittent monitoring in detecting vital sign abnormalities; however, the impact on clinical outcomes has not been established. METHODS: We performed a propensity-matched analysis of data describing patients admitted to general surgical wards between January 2018 and December 2019 at a single, tertiary medical centre in the USA. The primary outcome was a composite of in-hospital mortality or ICU transfer during hospitalisation. Secondary outcomes were the odds of individual components of the primary outcome, and heart failure, myocardial infarction, acute kidney injury, and rapid response team activations. Data are presented as odds ratios (ORs) with 95% confidence intervals (CIs) and n (%). RESULTS: We initially screened a population of 34,636 patients (mean age 58.3 (Range 18-101) yr, 16,456 (47.5%) women. After propensity matching, intermittent monitoring (n=12 345) was associated with increased risk of a composite of mortality or ICU admission (OR 3.42, 95% CI 3.19-3.67; P<0.001), and heart failure (OR 1.48, 95% CI 1.21-1.81; P<0.001), myocardial infarction (OR 3.87, 95% CI 2.71-5.71; P<0.001), and acute kidney injury (OR 1.32, 95% CI 1.09-1.57; P<0.001) compared with continuous wireless monitoring (n=7955). The odds of rapid response team intervention were similar in both groups (OR 0.86, 95% CI 0.79-1.06; P=0.726). CONCLUSIONS: Patients who received continuous ward monitoring were less likely to die or be admitted to ICU than those who received intermittent monitoring. These findings should be confirmed in prospective randomised trials.

Contactless Heart and Respiration Rates Estimation and Classification of Driver Physiological States Using CW Radar and Temporal Neural Networks.

The measurement and analysis of vital signs are a subject of significant research interest, particularly for monitoring the driver's physiological state, which is of crucial importance for road safety. Various approaches have been proposed using contact techniques to measure vital signs. However, all of these methods are invasive and cumbersome for the driver. This paper proposes using a non-contact sensor based on continuous wave (CW) radar at 24 GHz to measure vital signs. We associate these measurements with distinct temporal neural networks to analyze the signals to detect and extract heart and respiration rates as well as classify the physiological state of the driver. This approach offers robust performance in estimating the exact values of heart and respiration rates and in classifying the driver's physiological state. It is non-invasive and requires no physical contact with the driver, making it particularly practical and safe. The results presented in this paper, derived from the use of a 1D Convolutional Neural Network (1D-CNN), a Temporal Convolutional Network (TCN), a Recurrent Neural Network particularly the Bidirectional Long Short-Term Memory (Bi-LSTM), and a Convolutional Recurrent Neural Network (CRNN). Among these, the CRNN emerged as the most effective Deep Learning approach for vital signal analysis.

Implementing Wearable Sensors for Clinical Application at a Surgical Ward: Points to Consider before Starting.

Incorporating technology into healthcare processes is necessary to ensure the availability of high-quality care in the future. Wearable sensors are an example of such technology that could decrease workload, enable early detection of patient deterioration, and support clinical decision making by healthcare professionals. These sensors unlock continuous monitoring of vital signs, such as heart rate, respiration rate, blood oxygen saturation, temperature, and physical activity. However, broad and successful application of wearable sensors on the surgical ward is currently lacking. This may be related to the complexity, especially when it comes to replacing manual measurements by healthcare professionals. This report provides practical guidance to support peers before starting with the clinical application of wearable sensors in the surgical ward. For this purpose, the Non-Adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework of technology adoption and innovations in healthcare organizations is used, combining existing literature and our own experience in this field over the past years. Specifically, the relevant topics are discussed per domain, and key lessons are subsequently summarized.

Quantifying physiological stability in the general ward using continuous vital signs monitoring: the circadian kernel density estimator.

Technological advances seen in recent years have introduced the possibility of changing the way hospitalized patients are monitored by abolishing the traditional track-and-trigger systems and implementing continuous monitoring using wearable biosensors. However, this new monitoring paradigm raise demand for novel ways of analyzing the data streams in real time. The aim of this study was to design a stability index using kernel density estimation (KDE) fitted to observations of physiological stability incorporating the patients' circadian rhythm. Continuous vital sign data was obtained from two observational studies with 491 postoperative patients and 200 patients with acute exacerbation of chronic obstructive pulmonary disease. We defined physiological stability as the last 24 h prior to discharge. We evaluated the model against periods of eight hours prior to events defined either as severe adverse events (SAE) or as a total score in the early warning score (EWS) protocol of ≥ 6, ≥ 8, or ≥ 10. The results found good discriminative properties between stable physiology and EWS-events (area under the receiver operating characteristics curve (AUROC): 0.772-0.993), but lower for the SAEs (AUROC: 0.594-0.611). The time of early warning for the EWS events were 2.8-5.5 h and 2.5 h for the SAEs. The results showed that for severe deviations in the vital signs, the circadian KDE model can alert multiple hours prior to deviations being noticed by the staff. Furthermore, the model shows good generalizability to another cohort and could be a simple way of continuously assessing patient deterioration in the general ward.

Early Postoperative Vital Signs Predict Subsequent 90-Day Mortality After Pancreaticoduodenectomy.

BACKGROUND: While complication rates after pancreaticoduodenectomy (PD) have improved in recent decades, surgical-related death remains a possibility. Postoperative vital signs offer an untapped opportunity to identify predictors of 90-day mortality. METHODS: We performed a retrospective chart review interrogating postoperative day (POD 0-7) vital sign measurements from patients undergoing a PD at Thomas Jefferson University Hospital, Philadelphia, PA (2009-2014). Five specific vital signs were examined as predictors of mortality: temperature, heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure. Statistical analyses and logic algorithms were employed to rank vital sign parameters, with cut-points, to identify those associated with the highest risk of mortality and the most clinical relevance. RESULTS: In our cohort, 11/750 patients (1.5%) died within 30 days of surgery, and 21/750 patients (2.8%) died within 90 days of surgery. Vital sign perturbations associated with the highest risk of mortality included mean SBP < 95 mmHg on POD 7 (odds ratio 51.46) and the mean temperature < 96.9℉ on POD 3 (odds ratio 22.63) with specificities exceeding 99%. The most clinically relevant predictor (i.e., a higher sensitivity) was DBP < 60.5 mmHg on POD 7 (odds ratio 12.45, sensitivity of 75%). These predictors remained statistically significant in a multivariable model. CONCLUSIONS: Vital signs can be more effectively utilized to predict 90-day mortality after pancreaticoduodenectomy. Values beyond an informative threshold can potentially identify patients for more intensive monitoring with a goal of rescuing patients and preventing death.

Early warning scores and trigger recommendations must be used with care in older home nursing care patients: Results from an observational study.

AIMS: To explore modified early warning scores (MEWSs) and deviating vital signs among older home nursing care patients to determine whether the MEWS trigger recommendations were adhered to in cases of where registered nurses (RNs) suspected acute functional decline. DESIGN: Prospective observational study with a descriptive, explorative design. METHODS: Participants were included from April 2018 to February 2019. Demographic, health-related and clinical data were collected over a 3-month period. RESULTS: In all, 135 older patients participated. Median MEWS (n = 444) was 1 (interquartile range (IQR) 1-2). Frequently deviating vital signs were respiratory (88.8%) and heart rate (15.3%). Median habitual MEWS (n = 51) was 1 (IQR 0-1). Deviating vital signs were respiratory (72.5%) and heart rate (19.6%). A significant difference between habitual MEWS and MEWS recorded in cases of suspected functional decline was found (p = 0.002). MEWS' trigger recommendations were adhered to in 68.9% of all MEWS measurements.

Continuous monitoring is superior to manual measurements in detecting vital sign deviations in patients with COVID-19.

BACKGROUND: Patients admitted to the emergency care setting with COVID-19-infection can suffer from sudden clinical deterioration, but the extent of deviating vital signs in this group is still unclear. Wireless technology monitors patient vital signs continuously and might detect deviations earlier than intermittent measurements. The aim of this study was to determine frequency and duration of vital sign deviations using continuous monitoring compared to manual measurements. A secondary analysis was to compare deviations in patients admitted to ICU or having fatal outcome vs. those that were not. METHODS: Two wireless sensors continuously monitored (CM) respiratory rate (RR), heart rate (HR), and peripheral arterial oxygen saturation (SpO2 ). Frequency and duration of vital sign deviations were compared with point measurements performed by clinical staff according to regional guidelines, the National Early Warning Score (NEWS). RESULTS: SpO2 < 92% for more than 60 min was detected in 92% of the patients with CM vs. 40% with NEWS (p < .00001). RR > 24 breaths per minute for more than 5 min were detected in 70% with CM vs. 33% using NEWS (p = .0001). HR ≥ 111 for more than 60 min was seen in 51% with CM and 22% with NEWS (p = .0002). Patients admitted to ICU or having fatal outcome had longer durations of RR > 24 brpm (p = .01), RR > 21 brpm (p = .01), SpO2 < 80% (p = .01), and SpO2 < 85% (p = .02) compared to patients that were not. CONCLUSION: Episodes of desaturation and tachypnea in hospitalized patients with COVID-19 infection are common and often not detected by routine measurements.

Using Vital Signs to Place Acutely Ill Patients Quickly and Easily into Clinically Helpful Pathophysiologic Categories: Derivation and Validation of Eight Pathophysiologic Categories in Two Distinct Patient Populations of Acutely Ill Patients.

BACKGROUND: Early warning scores reliably identify patients at risk of imminent death, but do not provide insight into what may be wrong with the patient or what to do about it. OBJECTIVE: Our aim was to explore whether the Shock Index (SI), pulse pressure (PP), and ROX Index can place acutely ill medical patients in pathophysiologic categories that could indicate the interventions required. METHODS: A retrospective post-hoc analysis of previously obtained and reported clinical data for 45,784 acutely ill medical patients admitted to a major regional referral Canadian hospital between 2005 and 2010 and validated on 107,546 emergency admissions to four Dutch hospitals between 2017 and 2022. RESULTS: SI, PP, and ROX values divided patients into eight mutually exclusive physiologic categories. Mortality was highest in patient categories that included ROX Index value < 22, and a ROX Index value < 22 multiplied the risk of any other abnormality. Patients with a ROX Index value < 22, PP < 42 mm Hg, and SI > 0.7 had the highest mortality and accounted for 40% of deaths within 24 h of admission, whereas patients with a PP ≥ 42 mm Hg, SI ≤ 0.7, and ROX Index value ≥ 22 had the lowest risk of death. These results were the same in both the Canadian and Dutch patient cohorts. CONCLUSIONS: SI, PP, and ROX Index values can place acutely ill medical patients into eight mutually exclusive pathophysiologic categories with different mortality rates. Future studies will assess the interventions needed by these categories and their value in guiding treatment and disposition decisions.

Detecting Patient Deterioration Early Using Continuous Heart rate and Respiratory rate Measurements in Hospitalized COVID-19 Patients.

BACKGROUND: Presenting symptoms of COVID-19 patients are unusual compared with many other illnesses. Blood pressure, heart rate, and respiratory rate may stay within acceptable ranges as the disease progresses. Consequently, intermittent monitoring does not detect deterioration as it is happening. We investigated whether continuously monitoring heart rate and respiratory rate enables earlier detection of deterioration compared with intermittent monitoring, or introduces any risks. METHODS: When available, patients admitted to a COVID-19 ward received a wireless wearable sensor which continuously measured heart rate and respiratory rate. Two intensive care unit (ICU) physicians independently assessed sensor data, indicating when an intervention might be necessary (alarms). A third ICU physician independently extracted clinical events from the electronic medical record (EMR events). The primary outcome was the number of true alarms. Secondary outcomes included the time difference between true alarms and EMR events, interrater agreement for the alarms, and severity of EMR events that were not detected. RESULTS: In clinical practice, 48 (EMR) events occurred. None of the 4 ICU admissions were detected with the sensor. Of the 62 sensor events, 13 were true alarms (also EMR events). Of these, two were related to rapid response team calls. The true alarms were detected 39 min (SD = 113) before EMR events, on average. Interrater agreement was 10%. Severity of the 38 non-detected events was similar to the severity of 10 detected events. CONCLUSION: Continuously monitoring heart rate and respiratory rate does not reliably detect deterioration in COVID-19 patients when assessed by ICU physicians.